Enhancing product concept image generation through semantic feature prompts and LoRA training.

This paper proposes an innovative strategy that integrates fine-grained semantic feature decoding with Low-Rank Adaptation (LoRA) fine-tuning model training to significantly improve the performance of text-to-image technology, addressing the limitations of current Generative Artificial Intelligence (GAI) in product conceptual image design. Firstly, semantic information pertinent to product design is collected, and the E-Prime software is utilized to conduct a semantic priming task for extracting key semantic words. Subsequently, the DeepSeek prompt engineering method is employed to decode the fine-grained features of semantic words sequentially from abstract to concrete based on the three dimensions of mental image, functional image, and physical image. Semantic feature prompts are derived by expert evaluation and clustering methods. Finally, the LoRA technique is employed to train the dataset independently based on the semantic feature prompts, achieving the optimal model configuration. Taking the intelligent pulse diagnostic instrument as an example, the application of this strategy in product conceptual design is demonstrated. Furthermore, multi-dimensional assessments of text-to-image outcomes are conducted through comparative experiments, verifying the potential and efficacy of the proposed strategy, which provides a solution for the controlled generation of large models in product design applications.